Machine Learning and Knowledge Discovery in Databases

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European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20,2019, Proceedings, Part II, Lecture Notes in Computer Science 11907 – Lecture Notes in Artificial Intelligence

ISBN: 3030461467
ISBN 13: 9783030461461
Herausgeber: Ulf Brefeld/Elisa Fromont/Andreas Hotho et al
Verlag: Springer Verlag GmbH
Umfang: xxvi, 732 S., 123 s/w Illustr., 191 farbige Illustr., 732 p. 314 illus., 191 illus. in color.
Erscheinungsdatum: 02.05.2020
Auflage: 1/2020
Produktform: Kartoniert
Einband: Kartoniert

Chapter „Incorporating Dependencies in Spectral Kernels for Gaussian Processes“ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Artikelnummer: 8922952 Kategorie:

Beschreibung

The three volume proceedings LNAI 11906 - 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019.The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track.Chapter "Incorporating Dependencies in Spectral Kernels for Gaussian Processes" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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